Leafmap

Leafmap Examples¶

In [1]:

import leafmap
import geopandas as gpd

import leafmap import geopandas as gpd

In [2]:

m = leafmap.Map()
url = "https://nominatim.openstreetmap.org/search?format=json&q={s}"
m.add_search_control(url=url, zoom=10, position="topleft")
m

m = leafmap.Map() url = "https://nominatim.openstreetmap.org/search?format=json&q={s}" m.add_search_control(url=url, zoom=10, position="topleft") m

Out[2]:

In [3]:

m.clear_controls()
m

m.clear_controls() m

Out[3]:

In [4]:

m.add_wms_layer(
    url="https://services.terrascope.be/wms/v2?",
    layers="WORLDCOVER_2021_MAP",
    name="World Cover",
)
m.add_legend(title="World Cover", builtin_legend="ESA_WorldCover")
m

m.add_wms_layer( url="https://services.terrascope.be/wms/v2?", layers="WORLDCOVER_2021_MAP", name="World Cover", ) m.add_legend(title="World Cover", builtin_legend="ESA_WorldCover") m

Out[4]:

In [5]:

url = "https://github.com/opengeos/datasets/releases/download/places/wa_building_centroids.geojson"
gdf = gpd.read_file(url)

gdf["latitude"] = gdf.geometry.y
gdf["longitude"] = gdf.geometry.x

m = leafmap.Map(center=(gdf.latitude.mean(), gdf.longitude.mean()), zoom=15)

m.add_points_from_xy(
    gdf, x="longitude", y="latitude", cluster=True, layer_name="Building Centroids"
)

m

url = "https://github.com/opengeos/datasets/releases/download/places/wa_building_centroids.geojson" gdf = gpd.read_file(url) gdf["latitude"] = gdf.geometry.y gdf["longitude"] = gdf.geometry.x m = leafmap.Map(center=(gdf.latitude.mean(), gdf.longitude.mean()), zoom=15) m.add_points_from_xy( gdf, x="longitude", y="latitude", cluster=True, layer_name="Building Centroids" ) m

Out[5]:

In [6]:

m.add_circle_markers_from_xy(
    data="https://github.com/opengeos/datasets/releases/download/places/wa_building_centroids.geojson",
    radius=5,
    color="red",
    fill_color="yellow",
    fill_opacity=0.8,
)
m

m.add_circle_markers_from_xy( data="https://github.com/opengeos/datasets/releases/download/places/wa_building_centroids.geojson", radius=5, color="red", fill_color="yellow", fill_opacity=0.8, ) m

Out[6]:

In [7]:

m = leafmap.Map()
url = "https://github.com/opengeos/datasets/releases/download/places/wa_overture_buildings.geojson"
style = {"color": "red", "fillColor": "None"}
m.add_vector(url, style=style, layer_name="Roads", zoom_to_layer=True)
m

m = leafmap.Map() url = "https://github.com/opengeos/datasets/releases/download/places/wa_overture_buildings.geojson" style = {"color": "red", "fillColor": "None"} m.add_vector(url, style=style, layer_name="Roads", zoom_to_layer=True) m

Out[7]:

In [8]:

m = leafmap.Map()
data = "https://github.com/opengeos/datasets/releases/download/places/las_vegas_roads.geojson"
style = {"color": "red", "weight": 2}
m.add_vector(
    data, style=style, layer_name="Roads", info_mode="on_hover", zoom_to_layer=True
)
m

m = leafmap.Map() data = "https://github.com/opengeos/datasets/releases/download/places/las_vegas_roads.geojson" style = {"color": "red", "weight": 2} m.add_vector( data, style=style, layer_name="Roads", info_mode="on_hover", zoom_to_layer=True ) m

Out[8]:

In [9]:

m = leafmap.Map()
data = "https://github.com/opengeos/datasets/releases/download/us/us_counties.geojson"
m.add_data(
    data,
    column="CENSUSAREA",
    scheme="Quantiles",
    cmap="Blues",
    legend_title="Population",
)
m

m = leafmap.Map() data = "https://github.com/opengeos/datasets/releases/download/us/us_counties.geojson" m.add_data( data, column="CENSUSAREA", scheme="Quantiles", cmap="Blues", legend_title="Population", ) m

Out[9]:

In [10]:

url = (
    "https://github.com/opengeos/datasets/releases/download/raster/Libya-2023-07-01.tif"
)
url2 = (
    "https://github.com/opengeos/datasets/releases/download/raster/Libya-2023-09-13.tif"
)
m = leafmap.Map()
m.split_map(
    left_layer=url,
    right_layer=url2,
    left_label="Pre-event",
    right_label="Post-event",
)
m

url = ( "https://github.com/opengeos/datasets/releases/download/raster/Libya-2023-07-01.tif" ) url2 = ( "https://github.com/opengeos/datasets/releases/download/raster/Libya-2023-09-13.tif" ) m = leafmap.Map() m.split_map( left_layer=url, right_layer=url2, left_label="Pre-event", right_label="Post-event", ) m

Out[10]: